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Found 11 papers, 2 papers with code
Coherent Temporal Synthesis for Incremental Action Segmentation
Data replay is a successful incremental learning technique for images.
Every Mistake Counts in Assembly
Our framework constructs a knowledge base with spatial and temporal beliefs based on observed mistakes.
Temporal Action Segmentation: An Analysis of Modern Techniques
Temporal action segmentation (TAS) in videos aims at densely identifying video frames in minutes-long videos with multiple action classes.
Leveraging Action Affinity and Continuity for Semi-supervised Temporal Action Segmentation
To this end, we propose two novel loss functions for the unlabelled data: an action affinity loss and an action continuity loss.
Rapid Person Re-Identification via Sub-space Consistency Regularization
To strike a balance between the model accuracy and efficiency, we propose a novel Sub-space Consistency Regularization (SCR) algorithm that can speed up the ReID procedure by $0. 25$ times than real-value features under the same dimensions whilst maintaining a competitive accuracy, especially under short codes.
Sensitivity of Optimal Retirement Problem to Liquidity Constraints
In this work we analytically solve an optimal retirement problem, in which the agent optimally allocates the risky investment, consumption and leisure rate to maximise a gain function characterised by a power utility function of consumption and leisure, through the duality method.
Temporal Action Segmentation with High-level Complex Activity Labels
Due to the lack of action-level supervision, we adopt the Hungarian matching algorithm to relate latent action prototypes to ground truth semantic classes for evaluation.
Effect of Labour Income on the Optimal Bankruptcy Problem
In our framework, the agent is endowed by an initial debt, and she is required to repay her debt continuously.
Towards better Validity: Dispersion based Clustering for Unsupervised Person Re-identification
With this insight, we design a novel Dispersion-based Clustering (DBC) approach which can discover the underlying patterns in data.
Ranked #19 on
Unsupervised Person Re-Identification
on Market-1501
Feature Affinity based Pseudo Labeling for Semi-supervised Person Re-identification
Our approach measures the affinity of unlabeled samples with the underlying clusters of labeled data samples using the intermediate feature representations from deep networks.
Let Features Decide for Themselves: Feature Mask Network for Person Re-identification
Person re-identification aims at establishing the identity of a pedestrian from a gallery that contains images of multiple people obtained from a multi-camera system.
